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11/14/2022
Contrastive learning for regression in multi-site brain age prediction
Building accurate Deep Learning (DL) models for brain age prediction is ...
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11/10/2022
Unbiased Supervised Contrastive Learning
Many datasets are biased, namely they contain easy-to-learn features tha...
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08/19/2022
Towards Efficient Capsule Networks
From the moment Neural Networks dominated the scene for image processing...
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07/19/2022
To update or not to update? Neurons at equilibrium in deep models
Recent advances in deep learning optimization showed that, with some a-p...
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07/05/2022
Disentangling private classes through regularization
Deep learning models are nowadays broadly deployed to solve an incredibl...
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04/26/2022
Unsupervised Learning of Unbiased Visual Representations
Deep neural networks are known for their inability to learn robust repre...
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04/04/2022
REM: Routing Entropy Minimization for Capsule Networks
Capsule Networks ambition is to build an explainable and biologically-in...
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07/12/2021
HEMP: High-order Entropy Minimization for neural network comPression
We formulate the entropy of a quantized artificial neural network as a d...
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03/02/2021
EnD: Entangling and Disentangling deep representations for bias correction
Artificial neural networks perform state-of-the-art in an ever-growing n...
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02/10/2021
Dysplasia grading of colorectal polyps through CNN analysis of WSI
Colorectal cancer is a leading cause of cancer death for both men and wo...
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02/07/2021
SeReNe: Sensitivity based Regularization of Neurons for Structured Sparsity in Neural Networks
Deep neural networks include millions of learnable parameters, making th...
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01/25/2021
A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images
Early screening of patients is a critical issue in order to assess immed...
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01/25/2021
UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading
Histopathological characterization of colorectal polyps allows to tailor...
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01/15/2021
Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke
Purpose: In this study we investigate whether a Convolutional Neural Net...
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11/16/2020
LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks
LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training...
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08/04/2020
A non-discriminatory approach to ethical deep learning
Artificial neural networks perform state-of-the-art in an ever-growing n...
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04/30/2020
Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima
Recently, a race towards the simplification of deep networks has begun, ...
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04/11/2020
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
The possibility to use widespread and simple chest X-ray (CXR) imaging f...
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07/19/2019
Post-synaptic potential regularization has potential
Improving generalization is one of the main challenges for training deep...
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02/27/2018
Graph-based Image Anomaly Detection
RX Detector is recognized as the benchmark algorithm for image anomaly d...
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07/24/2017
